Caricature, a type of exaggerated artistic portrait, amplifies the
distinctive, yet nuanced traits of human faces. This task is typically left to
artists, as it has proven difficult to capture subjects' unique characteristics
well using automated methods. Recent development of deep end-to-end methods has
achieved promising results in capturing style and higher-level exaggerations.
However, a key part of caricatures, face warping, has remained challenging for
these systems. In this work, we propose AutoToon, the first supervised deep
learning method that yields high-quality warps for the warping component of
caricatures. Completely disentangled from style, it can be paired with any
stylization method to create diverse caricatures. In contrast to prior art, we
leverage an SENet and spatial transformer module and train directly on artist
warping fields, applying losses both prior to and after warping. As shown by
our user studies, we achieve appealing exaggerations that amplify
distinguishing features of the face while preserving facial detail.

本文介绍了 AutoToon，第一个通过深度学习提供高质量脸部变形的方法，这是卡通画的关键部分。与之前的方法不同，AutoToon 利用 SENet 和空间变换模块，直接在艺术家变形场上训练，通过前后损失来增强差异特征并保留面部细节。

AutoToon: 自动几何扭曲用于生成卡通脸部

AutoToon: Automatic Geometric Warping for Face Cartoon Generation

In this work, we describe a new deep learning based method that can
effectively distinguish AI-generated fake videos (referred to as {\em DeepFake}
videos hereafter) from real videos. Our method is based on the observations
that current DeepFake algorithm can only generate images of limited
resolutions, which need to be further warped to match the original faces in the
source video. Such transforms leave distinctive artifacts in the resulting
DeepFake videos, and we show that they can be effectively captured by
convolutional neural networks (CNNs). Compared to previous methods which use a
large amount of real and DeepFake generated images to train CNN classifier, our
method does not need DeepFake generated images as negative training examples
since we target the artifacts in affine face warping as the distinctive feature
to distinguish real and fake images. The advantages of our method are two-fold:
(1) Such artifacts can be simulated directly using simple image processing
operations on a image to make it as negative example. Since training a DeepFake
model to generate negative examples is time-consuming and resource-demanding,
our method saves a plenty of time and resources in training data collection;
(2) Since such artifacts are general existed in DeepFake videos from different
sources, our method is more robust compared to others. Our method is evaluated
on two sets of DeepFake video datasets for its effectiveness in practice.

本文通过深度学习和卷积神经网络技术识别 DeepFake 视频，并提出了一种不需要负面训练样本的方法，利用仿射变换中的瑕疵作为区分真假视频的显著特征，通过简单的图像处理操作即可模拟这些瑕疵。方法具有高效、节省时间和资源、鲁棒性等优点。